Associative memory is a fundamental property of the nervous system. It allows us to retrieve memories from partial or corrupted input, and thus plays a critical role in processing and categorizing information. The mechanisms that underlie associative memory are not yet understood. The leading model, a theoretical model, proposes that the nervous system performs associative memory by implementing attractor networks. In such networks, input, in the form of patterns of action potentials, provides partial information about a memory; the dynamics of the network then drives the neural activity to an attractor - a stable state in activity space - that corresponds to a complete representation of the memory. While the attractor model is a valuable construct, it is an idealized one - there is a large gap between the model and real neuronal networks. Our goal is to determine whether the attractor model is viable for real neuronal networks. The proposal is divided into two parts. The first is to determine whether networks with biologically -realistic properties can act as attractor networks. We will use analytical approaches, primarily mean field theory, to determine, in the abstract, the conditions necessary for the existence of attractors, and we will assess whether biological networks can satisfy these conditions. Large-scale simulations of realistic networks will then be performed to verify our analytical findings. The second is to determine how attractors can be learned. We will apply patterned input to a network, and examine network behavior as a function of the synaptic learning rule and the patterns of input to the network. In sum, our goal is to determine whether attractor networks can be realized in biological neuronal networks, and, if so, to determine the input patterns that will result in their formation. Successful completion of this project will provide us with experimentally testable predictions, predictions that can serve as a guide for investigating attractor networks in the nervous system.